The core function of signal transduction networks, the reliable transmission of information from cellular receptors to downstream effectors, can be adversely affected by biological noise. We propose addressing reliable signal transduction through the decomposition of noise into sources that are either intrinsic (reaction noise) or extrinsic (cell to cell variability) noise to the time scale of the signaling process. By dissectin noise mitigation mechanisms in signaling networks through a lens of `types of noise', we anticipate to gain deeper understanding into how mammalian signaling networks function under a regime of substantial noise. The central hypothesis guiding this research is that certain cellula mechanisms are more suitable in mitigating intrinsic noise while others serve to overcome extrinsic noise. This hypothesis will be tested through a systematic investigation of three noise mitigation mechanisms: network motifs, dynamic signals, and collective responses to determine their specific suitability to mitigate intrinsic and extrinsic noise sources. We propose the following aims: 1) To identify network-level feedbacks that prevent signal degradation due to intrinsic noise in a Mast cell model. We recently discovered a new pathway downstream of the FceRI receptor. Our preliminary data indicates that three previously unstudied network motifs that are important to the transmission of an oscillatory signal through the pathway. Using the oscillatory nature of this pathway, we will determine the ability of the three network motifs to specifically mitigate intrinsic noise. 2) We have developed a new statistical method to analyze the information transmission capacity of dynamic signals. Using this method we showed that the ability of the Erk signaling network to transmit dynamic signals substantially increases its information transmission capacity. We propose to determine the cause for increased information transmission capacity through dynamic signaling networks by analyzing the effect of different noise sources have on information transmission capacity. 3) To demonstrate the effect of extrinsic and intrinsic noise on the dose response curve of a noisy population. Due to nonlinearities in signaling networks the average response of a population of noisy cells could differ from the idealized noiseless single cell response. We will combine computational modeling, single cell dynamic measurement of Ca2+ and Erk response to ATP and EGF, respectively, to determine the effect noise has on the population level dose response curve. The proposed research will deliver key insights into the effects of intrinsic and extrinsic noise sources on signal transduction and how cells minimize the adverse effects of noise. Understanding how cells can function in regime with high noise will have important biomedical implications. Pharmacological manipulation of signaling networks is a common therapeutic strategy. Single cells studies show that biological noise causes high variability in cellular response that can be detrimental to the efficacy of the treatment. Insights into noise mitigation mechanisms will likely lead to new strategies that can increase the efficacy of many existing therapies that suffer from cellular response variability.